Overview

Dataset statistics

Number of variables29
Number of observations35549
Missing cells463770
Missing cells (%)45.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.9 MiB
Average record size in memory232.0 B

Variable types

CAT16
NUM12
UNSUPPORTED1

Reproduction

Analysis started2020-08-23 18:45:30.474993
Analysis finished2020-08-23 18:45:59.594990
Duration29.12 seconds
Software versionpandas-profiling v2.9.0rc1
Download configurationconfig.yaml

Warnings

tag has a high cardinality: 9358 distinct values High cardinality
ltag has a high cardinality: 971 distinct values High cardinality
yr is highly correlated with recordID and 1 other fieldsHigh correlation
recordID is highly correlated with yr and 1 other fieldsHigh correlation
period is highly correlated with recordID and 1 other fieldsHigh correlation
note1 has 31957 (89.9%) missing values Missing
species has 2015 (5.7%) missing values Missing
sex has 2506 (7.0%) missing values Missing
age has 20103 (56.6%) missing values Missing
reprod has 33898 (95.4%) missing values Missing
testes has 25857 (72.7%) missing values Missing
vagina has 33952 (95.5%) missing values Missing
pregnant has 34327 (96.6%) missing values Missing
nipples has 30521 (85.9%) missing values Missing
lactation has 35423 (99.6%) missing values Missing
hfl has 4111 (11.6%) missing values Missing
wgt has 3266 (9.2%) missing values Missing
tag has 2324 (6.5%) missing values Missing
note2 has 30965 (87.1%) missing values Missing
ltag has 1901 (5.3%) missing values Missing
note3 has 35533 (> 99.9%) missing values Missing
prevrt has 1780 (5.0%) missing values Missing
prevlet has 2071 (5.8%) missing values Missing
nestdir has 33718 (94.8%) missing values Missing
neststk has 30113 (84.7%) missing values Missing
note4 has 34908 (98.2%) missing values Missing
note5 has 32451 (91.3%) missing values Missing
prevlet is highly skewed (γ1 = 56.0844) Skewed
recordID has unique values Unique
prevrt is an unsupported type, check if it needs cleaning or further analysis Unsupported
prevlet has 33447 (94.1%) zeros Zeros
neststk has 3055 (8.6%) zeros Zeros

Variables

recordID
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count35549
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17775
Minimum1
Maximum35549
Zeros0
Zeros (%)0.0%
Memory size277.7 KiB
2020-08-23T13:45:59.687871image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1778.4
Q18888
median17775
Q326662
95-th percentile33771.6
Maximum35549
Range35548
Interquartile range (IQR)17774

Descriptive statistics

Standard deviation10262.3
Coefficient of variation (CV)0.577342
Kurtosis-1.2
Mean17775
Median Absolute Deviation (MAD)8887
Skewness0
Sum6.31883e+08
Variance1.05314e+08
MonotocityStrictly increasing
2020-08-23T13:45:59.840280image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20471< 0.1%
 
218241< 0.1%
 
259261< 0.1%
 
320691< 0.1%
 
300201< 0.1%
 
197791< 0.1%
 
177301< 0.1%
 
238731< 0.1%
 
341061< 0.1%
 
54161< 0.1%
 
Other values (35539)35539> 99.9%
 
ValueCountFrequency (%) 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
51< 0.1%
 
ValueCountFrequency (%) 
355491< 0.1%
 
355481< 0.1%
 
355471< 0.1%
 
355461< 0.1%
 
355451< 0.1%
 

mo
Real number (ℝ≥0)

Distinct count12
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.47402
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size277.7 KiB
2020-08-23T13:45:59.977891image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.39658
Coefficient of variation (CV)0.524648
Kurtosis-1.20493
Mean6.47402
Median Absolute Deviation (MAD)3
Skewness0.0514846
Sum230145
Variance11.5368
MonotocityNot monotonic
2020-08-23T13:46:00.082793image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
7363310.2%
 
434439.7%
 
333909.5%
 
530738.6%
 
1030648.6%
 
1130168.5%
 
1227997.9%
 
227967.9%
 
927517.7%
 
626977.6%
 
Other values (2)488713.7%
 
ValueCountFrequency (%) 
125187.1%
 
227967.9%
 
333909.5%
 
434439.7%
 
530738.6%
 
ValueCountFrequency (%) 
1227997.9%
 
1130168.5%
 
1030648.6%
 
927517.7%
 
823696.7%
 

dy
Real number (ℝ≥0)

Distinct count31
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.106
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Memory size277.7 KiB
2020-08-23T13:46:00.208071image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q19
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.25669
Coefficient of variation (CV)0.512648
Kurtosis-1.06417
Mean16.106
Median Absolute Deviation (MAD)7
Skewness0.0180591
Sum572551
Variance68.1729
MonotocityNot monotonic
2020-08-23T13:46:00.329845image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%) 
1618065.1%
 
2217785.0%
 
1517164.8%
 
917064.8%
 
1315164.3%
 
1414744.1%
 
2114554.1%
 
2514524.1%
 
413503.8%
 
2412963.6%
 
Other values (21)2000056.3%
 
ValueCountFrequency (%) 
17382.1%
 
25931.7%
 
37462.1%
 
413503.8%
 
512103.4%
 
ValueCountFrequency (%) 
316841.9%
 
3010202.9%
 
2911533.2%
 
289032.5%
 
277022.0%
 

yr
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count26
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1990.48
Minimum1977
Maximum2002
Zeros0
Zeros (%)0.0%
Memory size277.7 KiB
2020-08-23T13:46:00.461467image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1977
5-th percentile1979
Q11984
median1990
Q31997
95-th percentile2002
Maximum2002
Range25
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.49336
Coefficient of variation (CV)0.00376461
Kurtosis-1.28477
Mean1990.48
Median Absolute Deviation (MAD)7
Skewness-0.0441459
Sum7.07594e+07
Variance56.1504
MonotocityIncreasing
2020-08-23T13:46:00.578731image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%) 
199724937.0%
 
200222296.3%
 
198219785.6%
 
199617064.8%
 
198316734.7%
 
198716714.7%
 
200116104.5%
 
199816104.5%
 
198915694.4%
 
200015524.4%
 
Other values (16)1745849.1%
 
ValueCountFrequency (%) 
19775031.4%
 
197810482.9%
 
19797192.0%
 
198014154.0%
 
198114724.1%
 
ValueCountFrequency (%) 
200222296.3%
 
200116104.5%
 
200015524.4%
 
199911353.2%
 
199816104.5%
 

period
Real number (ℝ)

HIGH CORRELATION

Distinct count322
Unique (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149.534
Minimum-284
Maximum295
Zeros0
Zeros (%)0.0%
Memory size277.7 KiB
2020-08-23T13:46:00.710377image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-284
5-th percentile1
Q173
median149
Q3234
95-th percentile287
Maximum295
Range579
Interquartile range (IQR)161

Descriptive statistics

Standard deviation97.0927
Coefficient of variation (CV)0.649301
Kurtosis-0.313261
Mean149.534
Median Absolute Deviation (MAD)82
Skewness-0.408557
Sum5.31579e+06
Variance9427
MonotocityNot monotonic
2020-08-23T13:46:00.839092image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-44.53421.0%
 
2333120.9%
 
-623060.9%
 
2322900.8%
 
2302670.8%
 
2902600.7%
 
2312580.7%
 
2292560.7%
 
2882430.7%
 
2282380.7%
 
Other values (312)3277792.2%
 
ValueCountFrequency (%) 
-28412< 0.1%
 
-28312< 0.1%
 
-27812< 0.1%
 
-27712< 0.1%
 
-26710< 0.1%
 
ValueCountFrequency (%) 
2951640.5%
 
2941970.6%
 
2932180.6%
 
2922130.6%
 
291880.2%
 

plot
Real number (ℝ≥0)

Distinct count24
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.397
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Memory size277.7 KiB
2020-08-23T13:46:00.971918image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median11
Q317
95-th percentile22
Maximum24
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.79941
Coefficient of variation (CV)0.596596
Kurtosis-1.14782
Mean11.397
Median Absolute Deviation (MAD)6
Skewness0.121433
Sum405152
Variance46.2319
MonotocityNot monotonic
2020-08-23T13:46:01.088159image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%) 
1223656.7%
 
221946.2%
 
1720395.7%
 
119955.6%
 
419695.5%
 
919365.4%
 
1119185.4%
 
818915.3%
 
1418855.3%
 
318285.1%
 
Other values (14)1552943.7%
 
ValueCountFrequency (%) 
119955.6%
 
221946.2%
 
318285.1%
 
419695.5%
 
511943.4%
 
ValueCountFrequency (%) 
2410482.9%
 
235711.6%
 
2213993.9%
 
2111733.3%
 
2013903.9%
 

note1
Real number (ℝ≥0)

MISSING

Distinct count11
Unique (%)0.3%
Missing31957
Missing (%)89.9%
Infinite0
Infinite (%)0.0%
Mean7.3221
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Memory size277.7 KiB
2020-08-23T13:46:01.190006image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median6
Q313
95-th percentile13
Maximum13
Range12
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.21671
Coefficient of variation (CV)0.575888
Kurtosis-1.41534
Mean7.3221
Median Absolute Deviation (MAD)4
Skewness0.237883
Sum26301
Variance17.7807
MonotocityNot monotonic
2020-08-23T13:46:01.302268image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
1310683.0%
 
58582.4%
 
26881.9%
 
94271.2%
 
63761.1%
 
4700.2%
 
1580.2%
 
8280.1%
 
315< 0.1%
 
113< 0.1%
 
(Missing)3195789.9%
 
ValueCountFrequency (%) 
1580.2%
 
26881.9%
 
315< 0.1%
 
4700.2%
 
58582.4%
 
ValueCountFrequency (%) 
1310683.0%
 
121< 0.1%
 
113< 0.1%
 
94271.2%
 
8280.1%
 

stake
Real number (ℝ)

Distinct count80
Unique (%)0.2%
Missing70
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean44.8041
Minimum-99
Maximum99
Zeros8
Zeros (%)< 0.1%
Memory size277.7 KiB
2020-08-23T13:46:01.440464image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile12
Q125
median45
Q364
95-th percentile77
Maximum99
Range198
Interquartile range (IQR)39

Descriptive statistics

Standard deviation23.4533
Coefficient of variation (CV)0.523463
Kurtosis2.41726
Mean44.8041
Median Absolute Deviation (MAD)20
Skewness-0.4425
Sum1.58961e+06
Variance550.059
MonotocityNot monotonic
2020-08-23T13:46:01.595298image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
119332.6%
 
779252.6%
 
178862.5%
 
718592.4%
 
378322.3%
 
768302.3%
 
168212.3%
 
728122.3%
 
618062.3%
 
218012.3%
 
Other values (70)2697475.9%
 
ValueCountFrequency (%) 
-99940.3%
 
08< 0.1%
 
1360.1%
 
22< 0.1%
 
32< 0.1%
 
ValueCountFrequency (%) 
996931.9%
 
88190.1%
 
87250.1%
 
84350.1%
 
822< 0.1%
 

species
Categorical

MISSING

Distinct count47
Unique (%)0.1%
Missing2015
Missing (%)5.7%
Memory size277.7 KiB
DM
10596 
PP
3123 
DO
3027 
PB
2891 
RM
2609 
Other values (42)
11288 
ValueCountFrequency (%) 
DM1059629.8%
 
PP31238.8%
 
DO30278.5%
 
PB28918.1%
 
RM26097.3%
 
DS25047.0%
 
OT22496.3%
 
PF15974.5%
 
PE12993.7%
 
OL10062.8%
 
Other values (37)26337.4%
 
(Missing)20155.7%
 
2020-08-23T13:46:01.750461image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.05668
Min length2

sex
Categorical

MISSING

Distinct count5
Unique (%)< 0.1%
Missing2506
Missing (%)7.0%
Memory size277.7 KiB
M
17348 
F
15690 
R
 
3
Z
 
1
P
 
1
ValueCountFrequency (%) 
M1734848.8%
 
F1569044.1%
 
R3< 0.1%
 
Z1< 0.1%
 
P1< 0.1%
 
(Missing)25067.0%
 
2020-08-23T13:46:01.835090image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:46:01.972777image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length1
Mean length1.14099
Min length1

age
Categorical

MISSING

Distinct count3
Unique (%)< 0.1%
Missing20103
Missing (%)56.6%
Memory size277.7 KiB
Z
15441 
J
 
4
ZJ
 
1
ValueCountFrequency (%) 
Z1544143.4%
 
J4< 0.1%
 
ZJ1< 0.1%
 
(Missing)2010356.6%
 
2020-08-23T13:46:02.064892image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:46:02.178800image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.13103
Min length1

reprod
Categorical

MISSING

Distinct count4
Unique (%)0.2%
Missing33898
Missing (%)95.4%
Memory size277.7 KiB
J
1644 
S
 
3
R
 
3
M
 
1
ValueCountFrequency (%) 
J16444.6%
 
S3< 0.1%
 
R3< 0.1%
 
M1< 0.1%
 
(Missing)3389895.4%
 
2020-08-23T13:46:02.277775image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:46:02.403724image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.90711
Min length1

testes
Categorical

MISSING

Distinct count6
Unique (%)0.1%
Missing25857
Missing (%)72.7%
Memory size277.7 KiB
S
7530 
R
1449 
M
 
709
E
 
2
Z
 
1
ValueCountFrequency (%) 
S753021.2%
 
R14494.1%
 
M7092.0%
 
E2< 0.1%
 
Z1< 0.1%
 
J1< 0.1%
 
(Missing)2585772.7%
 
2020-08-23T13:46:02.500736image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:46:02.649914image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.45472
Min length1

vagina
Categorical

MISSING

Distinct count7
Unique (%)0.4%
Missing33952
Missing (%)95.5%
Memory size277.7 KiB
S
1420 
P
 
120
B
 
50
E
 
3
R
 
2
Other values (2)
 
2
ValueCountFrequency (%) 
S14204.0%
 
P1200.3%
 
B500.1%
 
E3< 0.1%
 
R2< 0.1%
 
Z1< 0.1%
 
51< 0.1%
 
(Missing)3395295.5%
 
2020-08-23T13:46:02.743945image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:46:02.909894image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.91015
Min length1

pregnant
Categorical

MISSING

Distinct count5
Unique (%)0.4%
Missing34327
Missing (%)96.6%
Memory size277.7 KiB
P
1179 
Q
 
36
E
 
5
S
 
1
L
 
1
ValueCountFrequency (%) 
P11793.3%
 
Q360.1%
 
E5< 0.1%
 
S1< 0.1%
 
L1< 0.1%
 
(Missing)3432796.6%
 
2020-08-23T13:46:03.007103image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:46:03.146894image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.93125
Min length1

nipples
Categorical

MISSING

Distinct count5
Unique (%)0.1%
Missing30521
Missing (%)85.9%
Memory size277.7 KiB
E
3988 
B
775 
R
 
252
S
 
10
P
 
3
ValueCountFrequency (%) 
E398811.2%
 
B7752.2%
 
R2520.7%
 
S10< 0.1%
 
P3< 0.1%
 
(Missing)3052185.9%
 
2020-08-23T13:46:03.244617image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:46:03.382493image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.71712
Min length1

lactation
Categorical

MISSING

Distinct count4
Unique (%)3.2%
Missing35423
Missing (%)99.6%
Memory size277.7 KiB
L
121 
E
 
3
S
 
1
B
 
1
ValueCountFrequency (%) 
L1210.3%
 
E3< 0.1%
 
S1< 0.1%
 
B1< 0.1%
 
(Missing)3542399.6%
 
2020-08-23T13:46:03.477745image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:46:03.604714image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.99291
Min length1

hfl
Real number (ℝ≥0)

MISSING

Distinct count56
Unique (%)0.2%
Missing4111
Missing (%)11.6%
Infinite0
Infinite (%)0.0%
Mean29.2879
Minimum2
Maximum70
Zeros0
Zeros (%)0.0%
Memory size277.7 KiB
2020-08-23T13:46:03.741706image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile16
Q121
median32
Q336
95-th percentile49
Maximum70
Range68
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.56476
Coefficient of variation (CV)0.326577
Kurtosis-0.606196
Mean29.2879
Median Absolute Deviation (MAD)6
Skewness0.317434
Sum920754
Variance91.4846
MonotocityNot monotonic
2020-08-23T13:46:03.879672image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
36407111.5%
 
3732779.2%
 
3526497.5%
 
2125047.0%
 
2023046.5%
 
2217044.8%
 
1615384.3%
 
2613593.8%
 
3413583.8%
 
1712853.6%
 
Other values (46)938926.4%
 
(Missing)411111.6%
 
ValueCountFrequency (%) 
21< 0.1%
 
62< 0.1%
 
72< 0.1%
 
83< 0.1%
 
93< 0.1%
 
ValueCountFrequency (%) 
701< 0.1%
 
641< 0.1%
 
582< 0.1%
 
572< 0.1%
 
561< 0.1%
 

wgt
Real number (ℝ≥0)

MISSING

Distinct count255
Unique (%)0.8%
Missing3266
Missing (%)9.2%
Infinite0
Infinite (%)0.0%
Mean42.6724
Minimum4
Maximum280
Zeros0
Zeros (%)0.0%
Memory size277.7 KiB
2020-08-23T13:46:04.028759image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile9
Q120
median37
Q348
95-th percentile132
Maximum280
Range276
Interquartile range (IQR)28

Descriptive statistics

Standard deviation36.6313
Coefficient of variation (CV)0.858429
Kurtosis6.13865
Mean42.6724
Median Absolute Deviation (MAD)14
Skewness2.33109
Sum1.37759e+06
Variance1341.85
MonotocityNot monotonic
2020-08-23T13:46:04.151499image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
468922.5%
 
448792.5%
 
458502.4%
 
438392.4%
 
478062.3%
 
428032.3%
 
107592.1%
 
487492.1%
 
97422.1%
 
497392.1%
 
Other values (245)2422568.1%
 
(Missing)32669.2%
 
ValueCountFrequency (%) 
417< 0.1%
 
5580.2%
 
61860.5%
 
75521.6%
 
86671.9%
 
ValueCountFrequency (%) 
2801< 0.1%
 
2781< 0.1%
 
2751< 0.1%
 
2741< 0.1%
 
2701< 0.1%
 

tag
Categorical

HIGH CARDINALITY
MISSING

Distinct count9358
Unique (%)28.2%
Missing2324
Missing (%)6.5%
Memory size277.7 KiB
0
 
1574
-1
 
117
110
 
43
4541
 
40
7470
 
39
Other values (9353)
31412 
ValueCountFrequency (%) 
015744.4%
 
-11170.3%
 
110430.1%
 
4541400.1%
 
7470390.1%
 
6660380.1%
 
8613370.1%
 
5588360.1%
 
8100PB350.1%
 
4645350.1%
 
Other values (9348)3123187.9%
 
(Missing)23246.5%
 
2020-08-23T13:46:04.306904image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.29194
Min length1

note2
Categorical

MISSING

Distinct count2
Unique (%)< 0.1%
Missing30965
Missing (%)87.1%
Memory size277.7 KiB
*
4582 
0
 
2
ValueCountFrequency (%) 
*458212.9%
 
02< 0.1%
 
(Missing)3096587.1%
 
2020-08-23T13:46:04.393495image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:46:04.492450image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.7421
Min length1

ltag
Categorical

HIGH CARDINALITY
MISSING

Distinct count971
Unique (%)2.9%
Missing1901
Missing (%)5.3%
Memory size277.7 KiB
0
31781 
6998
 
20
7051
 
18
6723
 
17
8587
 
17
Other values (966)
 
1795
ValueCountFrequency (%) 
03178189.4%
 
6998200.1%
 
7051180.1%
 
672317< 0.1%
 
858717< 0.1%
 
763017< 0.1%
 
570315< 0.1%
 
726015< 0.1%
 
665614< 0.1%
 
672614< 0.1%
 
Other values (961)17204.8%
 
(Missing)19015.3%
 
2020-08-23T13:46:04.628334image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length6
Median length1
Mean length1.26355
Min length1

note3
Categorical

MISSING

Distinct count1
Unique (%)6.2%
Missing35533
Missing (%)> 99.9%
Memory size277.7 KiB
*
16 
ValueCountFrequency (%) 
*16< 0.1%
 
(Missing)35533> 99.9%
 
2020-08-23T13:46:04.708274image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:46:04.798589image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9991
Min length1

prevrt
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing1780
Missing (%)5.0%
Memory size277.9 KiB

prevlet
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct count17
Unique (%)0.1%
Missing2071
Missing (%)5.8%
Infinite0
Infinite (%)0.0%
Mean2.57922
Minimum0
Maximum8000
Zeros33447
Zeros (%)94.1%
Memory size277.7 KiB
2020-08-23T13:46:04.927638image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8000
Range8000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation100.371
Coefficient of variation (CV)38.9154
Kurtosis3907.73
Mean2.57922
Median Absolute Deviation (MAD)0
Skewness56.0844
Sum86347
Variance10074.4
MonotocityNot monotonic
2020-08-23T13:46:05.049689image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
03344794.1%
 
21375< 0.1%
 
19984< 0.1%
 
23454< 0.1%
 
21083< 0.1%
 
80003< 0.1%
 
20362< 0.1%
 
23461< 0.1%
 
24121< 0.1%
 
20451< 0.1%
 
Other values (7)7< 0.1%
 
(Missing)20715.8%
 
ValueCountFrequency (%) 
03344794.1%
 
13591< 0.1%
 
19984< 0.1%
 
20171< 0.1%
 
20362< 0.1%
 
ValueCountFrequency (%) 
80003< 0.1%
 
45251< 0.1%
 
25001< 0.1%
 
24121< 0.1%
 
23461< 0.1%
 

nestdir
Categorical

MISSING

Distinct count15
Unique (%)0.8%
Missing33718
Missing (%)94.8%
Memory size277.7 KiB
SE
387 
E
256 
NE
250 
SW
237 
N
195 
Other values (10)
506 
ValueCountFrequency (%) 
SE3871.1%
 
E2560.7%
 
NE2500.7%
 
SW2370.7%
 
N1950.5%
 
W1880.5%
 
S1790.5%
 
NW1310.4%
 
AT2< 0.1%
 
51< 0.1%
 
Other values (5)5< 0.1%
 
(Missing)3371894.8%
 
2020-08-23T13:46:05.199611image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.92546
Min length1

neststk
Real number (ℝ)

MISSING
ZEROS

Distinct count82
Unique (%)1.5%
Missing30113
Missing (%)84.7%
Infinite0
Infinite (%)0.0%
Mean19.7564
Minimum-2
Maximum99
Zeros3055
Zeros (%)8.6%
Memory size277.7 KiB
2020-08-23T13:46:05.339065image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile0
Q10
median0
Q343
95-th percentile73
Maximum99
Range101
Interquartile range (IQR)43

Descriptive statistics

Standard deviation26.5344
Coefficient of variation (CV)1.34308
Kurtosis-0.346169
Mean19.7564
Median Absolute Deviation (MAD)0
Skewness1.0004
Sum107396
Variance704.075
MonotocityNot monotonic
2020-08-23T13:46:05.479935image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
030558.6%
 
461760.5%
 
511610.5%
 
371340.4%
 
711320.4%
 
75940.3%
 
22840.2%
 
52810.2%
 
14800.2%
 
61760.2%
 
Other values (72)13633.8%
 
(Missing)3011384.7%
 
ValueCountFrequency (%) 
-22< 0.1%
 
-19< 0.1%
 
030558.6%
 
115< 0.1%
 
22< 0.1%
 
ValueCountFrequency (%) 
99390.1%
 
889< 0.1%
 
879< 0.1%
 
862< 0.1%
 
852< 0.1%
 

note4
Categorical

MISSING

Distinct count6
Unique (%)0.9%
Missing34908
Missing (%)98.2%
Memory size277.7 KiB
TE
427 
TR
124 
UT
55 
TB
 
18
TA
 
11
ValueCountFrequency (%) 
TE4271.2%
 
TR1240.3%
 
UT550.2%
 
TB180.1%
 
TA11< 0.1%
 
TL6< 0.1%
 
(Missing)3490898.2%
 
2020-08-23T13:46:05.579874image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:46:05.729029image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.98197
Min length2

note5
Categorical

MISSING

Distinct count3
Unique (%)0.1%
Missing32451
Missing (%)91.3%
Memory size277.7 KiB
R
2288 
E
550 
D
260 
ValueCountFrequency (%) 
R22886.4%
 
E5501.5%
 
D2600.7%
 
(Missing)3245191.3%
 
2020-08-23T13:46:05.816636image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:46:05.936361image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.82571
Min length1

Interactions

2020-08-23T13:45:34.938856image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:35.106910image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:35.269054image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:35.435115image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:35.603091image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:35.767924image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:35.920593image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:36.091266image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:36.260816image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:36.425375image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:36.576970image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:36.741622image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:36.879602image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:37.030985image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:37.170992image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:37.317809image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:37.457925image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:37.607698image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:37.734056image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:37.875656image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:38.028915image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:38.178425image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:38.315649image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:38.458769image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:38.601482image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:38.761616image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:38.909059image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:39.062876image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:39.210395image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:39.363010image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:39.496428image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:39.644351image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:39.803253image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:39.958779image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:40.104171image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:40.251454image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:40.403999image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:40.555803image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:40.694170image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:40.836272image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:40.968771image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:41.111640image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:41.233735image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:41.373287image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:41.522835image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:41.668369image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:41.805004image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:41.945627image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:42.099193image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:42.259763image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:42.409363image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:42.561955image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:42.708895image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:42.875144image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:43.021139image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:43.179256image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:43.346345image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:43.508591image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:43.663650image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:43.828404image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:43.975990image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:44.131354image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:44.274721image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:44.420979image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:44.559008image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:44.711986image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:44.835716image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:44.974996image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:45.130025image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:45.280171image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:45.418928image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:45.562543image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:45.691200image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:45.831501image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:45.954352image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:46.082667image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:46.203471image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:46.333178image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:46.457700image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:46.582111image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:46.721220image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:46.837528image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:46.963059image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:47.085101image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:47.227087image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:47.393371image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:47.545963image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:47.707834image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:47.861423image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:48.030008image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:48.173451image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:48.331132image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:48.504799image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:48.673661image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:48.820269image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:48.975912image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:49.138716image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:49.298898image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:49.447916image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:49.602891image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:49.749325image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:49.912050image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:50.046280image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:50.174732image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:50.335052image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:50.491281image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:50.635396image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:50.768795image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:50.910432image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:51.061032image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:51.200653image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:51.343893image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:51.486989image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:51.649952image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:51.774650image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:51.912282image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:52.057892image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:52.200511image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:52.336191image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:52.476051image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:52.616649image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:52.799618image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:52.953207image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:53.113778image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:53.267366image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:53.437910image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:53.579218image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:53.711939image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:53.875771image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:54.017075image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:54.171023image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:54.327604image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:54.472218image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:54.600290image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:54.725789image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:54.855958image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:54.989976image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:55.122775image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:55.247628image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:55.374769image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:55.514416image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:55.641204image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:55.766551image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:55.906355image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-23T13:46:06.064788image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-23T13:46:06.303443image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-23T13:46:06.542459image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-23T13:46:06.803666image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-23T13:45:56.266742image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:57.798506image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:58.516769image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-23T13:45:59.320718image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

recordIDmodyyrperiodplotnote1stakespeciessexagereprodtestesvaginapregnantnippleslactationhflwgttagnote2ltagnote3prevrtprevletnestdirneststknote4note5
0171619771.02NaN16.0NaNMZNaNNaNNaNNaNNaNNaN32.0NaN0NaN0NaN00.0NaN0.0NaNNaN
1271619771.03NaN23.0NaNMZNaNNaNNaNNaNNaNNaN33.0NaN0NaN0NaN00.0NaN0.0NaNNaN
2371619771.02NaN25.0DMFNaNNaNNaNNaNNaNNaNNaN37.0NaN0NaN0NaN00.0NaN0.0NaNNaN
3471619771.07NaN25.0DMMZNaNNaNNaNNaNNaNNaN36.0NaN0NaN0NaN00.0NaN0.0NaNNaN
4571619771.03NaN26.0DMMZNaNNaNNaNNaNNaNNaN35.0NaN0NaN0NaN00.0NaN0.0NaNNaN
5671619771.01NaN27.0PFMNaNJNaNNaNNaNNaNNaN14.0NaN0NaN0NaN00.0NaN0.0NaNNaN
6771619771.02NaN31.0PEFNaNNaNNaNNaNPNaNNaNNaNNaN0NaN0NaN00.0NaN0.0NaNNaN
7871619771.01NaN36.0DMMNaNNaNSNaNNaNNaNNaN37.0NaN0NaN0NaN00.0NaN0.0NaNNaN
8971619771.01NaN42.0DMFZNaNNaNNaNNaNNaNNaN34.0NaN0NaN0NaN00.0NaN0.0NaNNaN
91071619771.06NaN46.0PFFZNaNNaNNaNNaNNaNNaN20.0NaN0NaN0NaN00.0NaN0.0NaNNaN

Last rows

recordIDmodyyrperiodplotnote1stakespeciessexagereprodtestesvaginapregnantnippleslactationhflwgttagnote2ltagnote3prevrtprevletnestdirneststknote4note5
355393554012312002295.015NaN54.0PBFZNaNNaNNaNNaNNaNNaN26.023.0482738NaN0NaN00.0NaNNaNNaNNaN
355403554112312002295.015NaN34.0PBFNaNNaNNaNNaNNaNRNaN24.031.0716537NaN0NaN00.0NaNNaNNaNNaN
355413554212312002295.015NaN23.0PBFZNaNNaNNaNNaNNaNNaN26.029.00F7659NaN0NaN00.0NaNNaNNaNNaN
355423554312312002295.015NaN77.0PBFZNaNNaNNaNNaNNaNNaN27.034.0701178NaN0NaN00.0NaNNaNNaNNaN
355433554412312002295.015NaN64.0USNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
355443554512312002295.015NaN32.0AHNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
355453554612312002295.015NaN45.0AHNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
355463554712312002295.010NaN32.0RMFNaNNaNNaNNaNNaNRNaN15.014.0NaNNaN0NaN00.0NaNNaNUTNaN
355473554812312002295.07NaN13.0DOMNaNNaNMNaNNaNNaNNaN36.051.0NaNNaN0NaN00.0NaNNaNUTNaN
355483554912312002295.052.099.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN